Interest in three dimension (3D) cameras is increasing as the popularity of 3D applications continues to grow in areas such as imaging, movies, games, computers, user interfaces, facial recognition, object recognition, augmented reality, and the like. A typical passive way to create 3D images is to use multiple cameras to capture stereo or multiple images. Using the stereo images, objects in the images can be triangulated to create the 3D image. One disadvantage with this triangulation technique is that it is difficult to create 3D images using small devices because there must be a minimum separation distance between each camera in order to create the three dimensional images. In addition, this technique is complex and therefore requires significant computer processing power in order to create the 3D images in real time.
For applications that require the acquisition of 3D images in real time, active depth imaging systems based on time-of-flight measurements are sometimes utilized. Time-of-flight cameras typically employ a light source that directs light at an object, a sensor that detects the light that is reflected from the object, and a processing unit that calculates the distance to the objected based on the round-trip time it takes for the light to travel to and from the object.
A continuing challenge with the acquisition of 3D images is balancing the desired performance parameters of the time-of-flight camera with the physical size and power constraints of the system. For example, the power requirements of time-of-flight systems meant for imaging near and far objects may be considerably different. These challenges are further complicated by extrinsic parameters (e.g., desired frame rate of the camera, depth resolution and lateral resolution) and intrinsic parameters (e.g., quantum efficiency of the sensor, fill factor, jitter, and noise).
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